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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： R Suguna S Deepa T Kanimozhi M Chandru G Arun Harshini M Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Agriculture is the backbone of India’s economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra Disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity.

关键词： Deep learning Productivity Plant diseases Microorganisms Accuracy Prediction algorithms Real-time systems Classification algorithms System analysis and design Residual neural networks

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AI-DRIVEN DAY-TO-DAY ROUTE CHOICE

arXiv

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arXiv 2024年

作者： Wang, Leizhen Duan, Peibo He, Zhengbing Lyu, Cheng Chen, Xin Zheng, Nan Yao, Li Ma, Zhenliang Department of Data Science and Artificial Intelligence Monash University Melbourne Australia Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge United States Chair of Transportation Systems Engineering Technical University of Munich Munich Germany School of Civil Engineering The University of Queensland Brisbane Australia Department of Civil Engineering Monash University Melbourne Australia School of Computer Science and Engineering Southeast University Nanjing China Department of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm Sweden

Understanding travelers’ route choices can help policymakers devise optimal operational and planning strategies for both normal and abnormal circumstances. However, existing choice modeling methods often rely on predefined assumptions and struggle to capture the dynamic and adaptive nature of travel behavior. Recently, Large Language Models (LLMs) have emerged as a promising alternative, demonstrating remarkable ability to replicate human-like behaviors across various fields. Despite this potential, their capacity to accurately simulate human route choice behavior in transportation contexts remains doubtful. To satisfy this curiosity, this paper investigates the potential of LLMs for route choice modeling by introducing an LLM-empowered agent, "LLMTraveler." This agent integrates an LLM as its core, equipped with a memory system that learns from past experiences and makes decisions by balancing retrieved data and personality traits. The study systematically evaluates the LLMTraveler’s ability to replicate human-like decision-making through two stages of day-to-day (DTD) congestion games: (1) analyzing its route-switching behavior in single origin-destination (OD) pair scenarios, where it demonstrates patterns that align with laboratory data but cannot be fully explained by traditional models, and (2) testing its capacity to model adaptive learning behaviors in multi-OD scenarios on the Ortuzar and Willumsen (OW) network, producing results comparable to Multinomial Logit (MNL) and Reinforcement Learning (RL) models. Additionally, the study assesses lightweight, open-source LLMs, highlighting their effectiveness in route choice simulation and their potential as cost-effective alternatives to more advanced closed-source models. These experiments demonstrate that the framework can partially replicate human-like decision-making in route choice while providing natural language explanations for its decisions. This capability offers valuable insights for transportation poli

关键词： Reinforcement learning

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Multi-Agent System based Medical Diagnosis Using Particle Swarm Optimization in Healthcare

Multi-Agent System based Medical Diagnosis Using Particle Sw...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Jagjit Singh Dhatterwal Mahaveer Singh Naruka Kuldeep Singh Kaswan Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India G L Bajaj Institute of Technology and Management Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India

Healthcare is an area that is challenging, dynamic, and diverse. Healthcare in the twenty-first century brings a variety of difficulties (population growth and rising healthcare costs). Compared to the conventional medical system, agent technology can offer superior healthcare for that. Agents in the hospital might deal with a variety of medical issues. We list collaboration across hospital wards, the development of diagnostics, the gathering of patient data, etc. as examples of issues that arise in hospitals. These issues can be resolved with a cooperative This paper proposes a generic architecture that integrates swarm intelligence with multi-agent healthcare systems to provide therapy as efficiently as possible.

关键词： Hospitals Sociology Semantics Medical services Medical diagnosis Particle swarm optimization Statistics

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SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Nfissi, Alaa Bouachir, Wassim Bouguila, Nizar Mishara, Brian Data Science Laboratory Montréal Canada Concordia University Concordia Institute for Information Systems Engineering Montréal Canada Centre for Research Intervention on Suicide Ethical Issues End-of-Life Practices Montréal Canada University of Québec at Montréal Psychology Department Montréal Canada

In the field of human-computer interaction and psychological assessment, speech emotion recognition (SER) plays an important role in deciphering emotional states from speech signals. Despite advancements, challenges persist due to system complexity, feature distinctiveness issues, and noise interference. This paper introduces a new end-to-end (E2E) deep learning multi-resolution framework for SER, addressing these limitations by extracting meaningful representations directly from raw waveform speech signals. By leveraging the properties of the fast discrete wavelet transform (FDWT), including the cascade algorithm, conjugate quadrature filter, and coefficient denoising, our approach introduces a learnable model for both wavelet bases and denoising through deep learning techniques. The framework incorporates an activation function for learnable asymmetric hard thresholding of wavelet coefficients. Our approach exploits the capabilities of wavelets for effective localization in both time and frequency domains. We then combine one-dimensional dilated convolutional neural networks (1D dilated CNN) with a spatial attention layer and bidirectional gated recurrent units (Bi-GRU) with a temporal attention layer to efficiently capture the nuanced spatial and temporal characteristics of emotional features. By handling variable-length speech without segmentation and eliminating the need for pre or post-processing, the proposed model outperformed state-of-the-art methods on IEMOCAP and EMO-DB datasets. The source code of this paper is shared on the Github repository: https://***/alaaNfissi/SigWavNet-Learning-Multiresolution-Signal-Wavelet-Network-for-Speech-Emotion-Recognition. © 2010-2012 IEEE.

关键词： Discrete wavelet transforms

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Real-Time Decision-Making Techniques using Artificial Intelligence and Cloud Computing

Real-Time Decision-Making Techniques using Artificial Intell...

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Disruptive Technologies (ICDT), International Conference on

作者： Kuldeep Singh Kaswan Jagjit Singh Dhatterwal Naresh Kumar Shashank Awasthi Sansar Singh Chauhan School of Computing Science & Engineering Galgotias University Greater Noida UP India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India GL Bajaj Institute of Technology & Management Greater Noida India

scientific research will increasingly rely on AI and the cloud in the future; our suggested solution will allow us to use these technologies to solve a number of problems (CC). We have outlined the different issues that may be addressed via the combined efforts of cloud computing and AI and discussed how to implement such an approach. One of the most powerful exploration techniques is, for example, using cloud-based artificial intelligence algorithms to increase productivity. Drive to create apps, manufactured in the cloud, beyond the fundamental automation pro, requires the ability to predict scenarios and make continuous decisions online. In this paper, we describe a programming language for intelligent computing that will enable machines to reason and make choices for themselves, in real time.

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Enhancing Effective Learning Capability of SOAR Agent based Episodic Memory

Enhancing Effective Learning Capability of SOAR Agent based ...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Kuldeep Singh Kaswan Mahaveer Singh Naruka Jagjit Singh Dhatterwal School of Computing Science and Engineering Galgotias University Greater Noida India G L Bajaj Institute of Technology and Management Greater Noida India Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

In order to increase the agent's intelligence, the fundamental goal of the research paper is to learn from its experiences. In order to solve a problem, learning is accomplished with the aid of episodic memory, which stores previous experiences as episodes. This study illustrates the improvement in intelligence with the use of the application of an example in soar. Working memory elements (WMEs), which are episodes (past experiences) in the form, are discussed in this paper as a means of enhancing intelligence. The purpose of this work is to demonstrate that knowledge always leads to performance improvement, meaning that as an agent studies more, performance will extend.

关键词： Memory architecture Learning (artificial intelligence) Encoding

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Harmonizing Energy Harvesting and Backscattering Communications Through Agile Power Allocation

Harmonizing Energy Harvesting and Backscattering Communicati...

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European Conference on Microwave

作者： Kai Xu Yishan Wang Wei Gong Jayakrishnan Methapettyparambu Purushothama Chaoyun Song George Goussetis Yuan Ding The Institute of Sensors Signals and Systems Heriot-Watt University U.K Institute for Digital Communication University of Edinburgh U.K. School of Computer Science and Technology and the School of Data Science University of Science and Technology of China China Department of Engineering Strand Campus King’s College London U.K.

ISBN: (数字)9782874870774

ISBN: (纸本)9798350385892

Backscatter communication (BackCom) emerges as a promising solution for the Internet of Things (IoT) ecosystem due to its low-power operation, which provides a potential for power autonomy through radio frequency (RF) energy harvesting (EH) technology. However, the dynamic power allocation tuning between communication and powering is still a challenge. To overcome it, in this paper we propose an agile power allocation strategy by integrating the EH circuit into backscatter modulator, i.e. employing it as one of the load elements in the backscatter tag. The power allocation can thus be dynamically adjusted in the time domain, utilizing a square wave with variable duty cycles to fine-tune the power ratio digitally. Our approach balances the backscatter system in communication and powering, providing a scalable solution adaptable to most existing BackCom systems.

关键词： Radio frequency Power dividers Modulation Microwave communication Resource management Internet of Things Energy harvesting Time-domain analysis Tuning Backscatter

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big data Analytics

A Comparative Analysis of Artificial Intelligence and Busine...

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Advances in Computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Bhagath Chandra Chowdari Marella Gopi Chand Vegineni Sireesha Addanki Ehsan Ellahi Arun Kumar K Rishabh Mandal Department of Financial Services Insights & Data Capgemini India Enrollment and Eligibility Team India System soft Technologies llc Information Technology India Department of Information Systems Cleveland State University Ohio United States Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system ( $\mathbf{92.6 \%}$ ) and the neural network (NNs) models ( ${92}.7 \%$ ). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans.

关键词： Radio frequency Machine learning algorithms Accuracy Artificial neural networks Big data Market research Classification algorithms Business intelligence Random forests Recruitment

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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： P. Rajasekar Alok Kumar Prema Latha V Jayapal N Boddu Soundarya Santhoshi K. Karthik Mathiyalagan Sivaramkumar Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electrical and Electronics Engineering Guru Gobind Singh Educational Society Technical Campus Bokaro Jharkhand India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of Mechanical Engineering S.R.K.R Engineering College Bhimavaram Andhra Pradesh India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of BIOSCIENCES Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems.

关键词： Location awareness Deep learning Fault detection Feature extraction data augmentation Electrical fault detection data models Convolutional neural networks Integrated circuit modeling Long short term memory

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： Smart power grids

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